Overview

Dataset statistics

Number of variables12
Number of observations10324
Missing cells0
Missing cells (%)0.0%
Duplicate rows206
Duplicate rows (%)2.0%
Total size in memory968.0 KiB
Average record size in memory96.0 B

Variable types

Numeric12

Alerts

Dataset has 206 (2.0%) duplicate rowsDuplicates
country_costmean is highly overall correlated with country_costsum and 8 other fieldsHigh correlation
country_costsum is highly overall correlated with country_costmean and 8 other fieldsHigh correlation
country_insmean is highly overall correlated with country_costmean and 8 other fieldsHigh correlation
country_inssum is highly overall correlated with country_costmean and 8 other fieldsHigh correlation
country_qtymean is highly overall correlated with country_costmean and 8 other fieldsHigh correlation
country_qtysum is highly overall correlated with country_costmean and 8 other fieldsHigh correlation
country_valmean is highly overall correlated with country_costmean and 8 other fieldsHigh correlation
country_valsum is highly overall correlated with country_costmean and 8 other fieldsHigh correlation
country_weightmean is highly overall correlated with country_costmean and 8 other fieldsHigh correlation
country_weightsum is highly overall correlated with country_costmean and 8 other fieldsHigh correlation

Reproduction

Analysis started2024-03-14 12:53:34.559322
Analysis finished2024-03-14 12:53:42.111325
Duration7.55 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

country_qtycount
Real number (ℝ)

Distinct98
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.07594
Minimum1
Maximum343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:42.152727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q158
median87
Q3142
95-th percentile298
Maximum343
Range342
Interquartile range (IQR)84

Descriptive statistics

Standard deviation75.924561
Coefficient of variation (CV)0.7090721
Kurtosis1.8483416
Mean107.07594
Median Absolute Deviation (MAD)39
Skewness1.393988
Sum1105452
Variance5764.539
MonotonicityNot monotonic
2024-03-14T18:23:42.213674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
343 343
 
3.3%
82 328
 
3.2%
298 298
 
2.9%
98 294
 
2.8%
93 279
 
2.7%
87 261
 
2.5%
77 231
 
2.2%
228 228
 
2.2%
75 225
 
2.2%
210 210
 
2.0%
Other values (88) 7627
73.9%
ValueCountFrequency (%)
1 13
 
0.1%
2 22
 
0.2%
3 45
0.4%
4 16
 
0.2%
5 40
0.4%
6 48
0.5%
7 21
 
0.2%
8 24
 
0.2%
9 9
 
0.1%
10 70
0.7%
ValueCountFrequency (%)
343 343
3.3%
298 298
2.9%
228 228
2.2%
210 210
2.0%
196 196
1.9%
185 185
1.8%
171 171
1.7%
167 167
1.6%
164 164
1.6%
149 149
1.4%

del_date_scheduled_yr
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.2576
Minimum2006
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:42.263919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2007
Q12009
median2011
Q32013
95-th percentile2015
Maximum2015
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4812415
Coefficient of variation (CV)0.0012336766
Kurtosis-1.1597924
Mean2011.2576
Median Absolute Deviation (MAD)2
Skewness-0.13721384
Sum20764223
Variance6.1565592
MonotonicityNot monotonic
2024-03-14T18:23:42.308779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2014 1528
14.8%
2012 1273
12.3%
2013 1272
12.3%
2009 1253
12.1%
2010 1204
11.7%
2008 1029
10.0%
2015 1017
9.9%
2011 1011
9.8%
2007 672
6.5%
2006 65
 
0.6%
ValueCountFrequency (%)
2006 65
 
0.6%
2007 672
6.5%
2008 1029
10.0%
2009 1253
12.1%
2010 1204
11.7%
2011 1011
9.8%
2012 1273
12.3%
2013 1272
12.3%
2014 1528
14.8%
2015 1017
9.9%
ValueCountFrequency (%)
2015 1017
9.9%
2014 1528
14.8%
2013 1272
12.3%
2012 1273
12.3%
2011 1011
9.8%
2010 1204
11.7%
2009 1253
12.1%
2008 1029
10.0%
2007 672
6.5%
2006 65
 
0.6%

country_qtysum
Real number (ℝ)

HIGH CORRELATION 

Distinct213
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1859474.4
Minimum14
Maximum15436074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:42.361999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15194
Q1311950
median1016903
Q32446649
95-th percentile6521076
Maximum15436074
Range15436060
Interquartile range (IQR)2134699

Descriptive statistics

Standard deviation2547577.7
Coefficient of variation (CV)1.3700526
Kurtosis13.085116
Mean1859474.4
Median Absolute Deviation (MAD)927549
Skewness3.1691201
Sum1.9197214 × 1010
Variance6.490152 × 1012
MonotonicityNot monotonic
2024-03-14T18:23:42.426068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65366 343
 
3.3%
595062 298
 
2.9%
15194 228
 
2.2%
3548124 210
 
2.0%
15436074 196
 
1.9%
2443751 185
 
1.8%
1515631 171
 
1.7%
3779574 167
 
1.6%
1145218 164
 
1.6%
5829963 149
 
1.4%
Other values (203) 8213
79.6%
ValueCountFrequency (%)
14 2
 
< 0.1%
50 3
 
< 0.1%
55 3
 
< 0.1%
80 1
 
< 0.1%
115 10
0.1%
140 3
 
< 0.1%
163 4
 
< 0.1%
165 2
 
< 0.1%
169 6
0.1%
200 3
 
< 0.1%
ValueCountFrequency (%)
15436074 196
1.9%
6909336 140
1.4%
6811335 105
1.0%
6521076 135
1.3%
5829963 149
1.4%
5176819 145
1.4%
5113821 83
0.8%
4576185 82
0.8%
4316624 82
0.8%
3988402 77
 
0.7%

country_qtymean
Real number (ℝ)

HIGH CORRELATION 

Distinct215
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18332.535
Minimum7
Maximum78755.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:42.489716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile190.57143
Q14395.1531
median12683.296
Q326875.3
95-th percentile58862.9
Maximum78755.48
Range78748.48
Interquartile range (IQR)22480.147

Descriptive statistics

Standard deviation18582.414
Coefficient of variation (CV)1.0136303
Kurtosis1.1920652
Mean18332.535
Median Absolute Deviation (MAD)10137.374
Skewness1.3261977
Sum1.8926509 × 108
Variance3.453061 × 108
MonotonicityNot monotonic
2024-03-14T18:23:42.555189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190.5714286 343
 
3.3%
1996.852349 298
 
2.9%
66.64035088 228
 
2.2%
16895.82857 210
 
2.0%
78755.47959 196
 
1.9%
13209.46486 185
 
1.8%
8863.339181 171
 
1.7%
22632.17964 167
 
1.6%
6983.036585 164
 
1.6%
39127.26846 149
 
1.4%
Other values (205) 8213
79.6%
ValueCountFrequency (%)
7 2
 
< 0.1%
13.10526316 19
0.2%
16.42857143 7
 
0.1%
18.33333333 3
 
< 0.1%
28.16666667 6
 
0.1%
38.33333333 3
 
< 0.1%
40.75 4
 
< 0.1%
41.83333333 24
0.2%
46.66666667 3
 
< 0.1%
50 3
 
< 0.1%
ValueCountFrequency (%)
78755.47959 196
1.9%
67511 55
 
0.5%
64869.85714 105
1.0%
62537.32609 46
 
0.4%
61612.3012 83
0.8%
58862.9 50
 
0.5%
55807.13415 82
0.8%
52641.7561 82
0.8%
51797.42857 77
 
0.7%
50769.55556 18
 
0.2%

country_valsum
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15267673
Minimum0
Maximum67742340
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:42.617896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile409852.36
Q12848863.5
median10804267
Q321597701
95-th percentile57488655
Maximum67742340
Range67742340
Interquartile range (IQR)18748837

Descriptive statistics

Standard deviation16510383
Coefficient of variation (CV)1.0813949
Kurtosis2.0040779
Mean15267673
Median Absolute Deviation (MAD)8856917.1
Skewness1.5874752
Sum1.5762345 × 1011
Variance2.7259274 × 1014
MonotonicityNot monotonic
2024-03-14T18:23:42.678347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1283746.29 343
 
3.3%
7079591.28 298
 
2.9%
468161.63 228
 
2.2%
33869606.52 210
 
2.0%
67742339.79 196
 
1.9%
35452389.84 185
 
1.8%
14919430.41 171
 
1.7%
28476197.72 167
 
1.6%
12069370.48 164
 
1.6%
26199468.38 149
 
1.4%
Other values (206) 8213
79.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
9.36 8
0.1%
187.28 10
0.1%
451.25 2
 
< 0.1%
630 1
 
< 0.1%
2064 2
 
< 0.1%
3580 3
 
< 0.1%
3600 1
 
< 0.1%
4080 7
0.1%
4617.35 3
 
< 0.1%
ValueCountFrequency (%)
67742339.79 196
1.9%
64697700.08 105
1.0%
59751187.71 135
1.3%
57488655.23 140
1.4%
55933176.76 145
1.4%
47265256.05 82
 
0.8%
40242848.23 83
 
0.8%
36047667.06 93
0.9%
35452389.84 185
1.8%
33869606.52 210
2.0%

country_valmean
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157650.57
Minimum0
Maximum616168.57
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:42.737704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3742.7006
Q141704.813
median110386.38
Q3222981.54
95-th percentile442601.39
Maximum616168.57
Range616168.57
Interquartile range (IQR)181276.73

Descriptive statistics

Standard deviation144066.26
Coefficient of variation (CV)0.91383283
Kurtosis0.72852973
Mean157650.57
Median Absolute Deviation (MAD)81248.156
Skewness1.1578605
Sum1.6275845 × 109
Variance2.0755089 × 1010
MonotonicityNot monotonic
2024-03-14T18:23:43.000408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3742.700554 343
 
3.3%
23757.01772 298
 
2.9%
2053.340482 228
 
2.2%
161283.8406 210
 
2.0%
345624.1826 196
 
1.9%
191634.5397 185
 
1.8%
87248.13105 171
 
1.7%
170516.154 167
 
1.6%
73593.72244 164
 
1.6%
175835.3583 149
 
1.4%
Other values (206) 8213
79.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.17 8
0.1%
18.728 10
0.1%
225.625 2
 
< 0.1%
582.8571429 7
 
0.1%
630 1
 
< 0.1%
815 6
 
0.1%
1032 2
 
< 0.1%
1152.631579 19
0.2%
1193.333333 3
 
< 0.1%
ValueCountFrequency (%)
616168.5722 105
1.0%
581951.2 10
 
0.1%
576405.5616 82
0.8%
574595.3337 46
 
0.4%
534416.7527 55
0.5%
492998.1667 18
 
0.2%
484853.5931 83
0.8%
444717.7256 50
 
0.5%
442601.3904 135
1.3%
437305.39 62
0.6%

country_inssum
Real number (ℝ)

HIGH CORRELATION 

Distinct214
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23223.585
Minimum0
Maximum123010.43
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:43.062338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile498.73
Q14740.65
median14598.43
Q331070.02
95-th percentile72852.29
Maximum123010.43
Range123010.43
Interquartile range (IQR)26329.37

Descriptive statistics

Standard deviation25539.994
Coefficient of variation (CV)1.0997438
Kurtosis3.0096955
Mean23223.585
Median Absolute Deviation (MAD)12591.67
Skewness1.703769
Sum2.3976029 × 108
Variance6.522913 × 108
MonotonicityNot monotonic
2024-03-14T18:23:43.124119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1533.55 343
 
3.3%
13904.9 298
 
2.9%
535.39 228
 
2.2%
72852.29 210
 
2.0%
123010.43 196
 
1.9%
69622.1 185
 
1.8%
31461.79 171
 
1.7%
35536.51 167
 
1.6%
14407.37005 164
 
1.6%
56469.83 149
 
1.4%
Other values (204) 8213
79.6%
ValueCountFrequency (%)
0 9
0.1%
0.00245 2
 
< 0.1%
0.0049 2
 
< 0.1%
0.0098 4
 
< 0.1%
0.01225 10
0.1%
0.0245 10
0.1%
0.02695 11
0.1%
0.0294 12
0.1%
0.0343 14
0.1%
0.37 10
0.1%
ValueCountFrequency (%)
123010.43 196
1.9%
82614.19 140
1.4%
72852.29 210
2.0%
71458.04 145
1.4%
69622.1 185
1.8%
67697.39 105
1.0%
63861.56 135
1.3%
62443.64 93
0.9%
60052.38 83
 
0.8%
56469.83 149
1.4%

country_insmean
Real number (ℝ)

HIGH CORRELATION 

Distinct207
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.44259
Minimum0
Maximum863.37778
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:43.184763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.4709913
Q166.728333
median177.64917
Q3367.78444
95-th percentile644.73705
Maximum863.37778
Range863.37778
Interquartile range (IQR)301.05611

Descriptive statistics

Standard deviation206.33589
Coefficient of variation (CV)0.8838828
Kurtosis-0.21052914
Mean233.44259
Median Absolute Deviation (MAD)132.19583
Skewness0.8788836
Sum2410061.3
Variance42574.5
MonotonicityNot monotonic
2024-03-14T18:23:43.243923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.470991254 343
 
3.3%
46.66073826 298
 
2.9%
2.348201754 228
 
2.2%
346.9156667 210
 
2.0%
627.6042347 196
 
1.9%
376.3356757 185
 
1.8%
183.987076 171
 
1.7%
212.7934731 167
 
1.6%
87.84981738 164
 
1.6%
378.9921477 149
 
1.4%
Other values (197) 8213
79.6%
ValueCountFrequency (%)
0 9
 
0.1%
0.00245 65
0.6%
0.037 10
 
0.1%
0.265 2
 
< 0.1%
1.04 1
 
< 0.1%
1.051428571 7
 
0.1%
1.215 2
 
< 0.1%
1.383333333 6
 
0.1%
1.68147 5
 
< 0.1%
1.81 3
 
< 0.1%
ValueCountFrequency (%)
863.3777778 18
 
0.2%
859.8846774 62
0.6%
723.5226506 83
0.8%
719.873 10
 
0.1%
671.4369892 93
0.9%
661.0059701 67
0.6%
650.1213415 82
0.8%
644.7370476 105
1.0%
635.0326087 46
0.4%
631.2508696 23
 
0.2%

country_weightsum
Real number (ℝ)

HIGH CORRELATION 

Distinct217
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540707.61
Minimum22.066213
Maximum4149553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:43.302424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum22.066213
5-th percentile9844.8752
Q190420.547
median240889.72
Q3716202.34
95-th percentile2735925.6
Maximum4149553
Range4149530.9
Interquartile range (IQR)625781.79

Descriptive statistics

Standard deviation776784.31
Coefficient of variation (CV)1.4366069
Kurtosis8.0659364
Mean540707.61
Median Absolute Deviation (MAD)199482.98
Skewness2.7244361
Sum5.5822654 × 109
Variance6.0339387 × 1011
MonotonicityNot monotonic
2024-03-14T18:23:43.359536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11088.71732 343
 
3.3%
90420.54667 298
 
2.9%
2068.879046 228
 
2.2%
586052.9309 210
 
2.0%
2735925.636 196
 
1.9%
1079001.271 185
 
1.8%
394580.7796 171
 
1.7%
937767.9118 167
 
1.6%
174738.0976 164
 
1.6%
929607.9252 149
 
1.4%
Other values (207) 8213
79.6%
ValueCountFrequency (%)
22.06621257 2
< 0.1%
31.26923077 1
 
< 0.1%
34.34329251 2
< 0.1%
86.35614681 2
< 0.1%
97.39517977 3
< 0.1%
104.9684961 2
< 0.1%
112.8086766 1
 
< 0.1%
122.6546161 2
< 0.1%
135.9775433 1
 
< 0.1%
182.0935448 1
 
< 0.1%
ValueCountFrequency (%)
4149552.998 145
1.4%
3442204.674 82
0.8%
3248952.975 105
1.0%
2735925.636 196
1.9%
2054455.384 18
 
0.2%
1683935.523 135
1.3%
1682090.663 23
 
0.2%
1622786.905 97
0.9%
1531660.806 140
1.4%
1478512.788 21
 
0.2%

country_weightmean
Real number (ℝ)

HIGH CORRELATION 

Distinct217
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6108.9177
Minimum9.0740309
Maximum114136.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:43.416600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum9.0740309
5-th percentile32.328622
Q11223.949
median2997.078
Q37310.1482
95-th percentile16938.648
Maximum114136.41
Range114127.34
Interquartile range (IQR)6086.1992

Descriptive statistics

Standard deviation9822.0179
Coefficient of variation (CV)1.6078164
Kurtosis39.819736
Mean6108.9177
Median Absolute Deviation (MAD)2430.0001
Skewness5.2429587
Sum63068466
Variance96472035
MonotonicityNot monotonic
2024-03-14T18:23:43.476107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.32862192 343
 
3.3%
303.4246533 298
 
2.9%
9.074030902 228
 
2.2%
2790.728243 210
 
2.0%
13958.80426 196
 
1.9%
5832.439302 185
 
1.8%
2307.489939 171
 
1.7%
5615.376718 167
 
1.6%
1065.476205 164
 
1.6%
6238.979364 149
 
1.4%
Other values (207) 8213
79.6%
ValueCountFrequency (%)
9.074030902 228
2.2%
11.03310628 2
 
< 0.1%
17.17164625 2
 
< 0.1%
25.64342439 8
 
0.1%
31.26923077 1
 
< 0.1%
32.32862192 343
3.3%
32.46505992 3
 
< 0.1%
34.59694459 19
 
0.2%
36.80701992 7
 
0.1%
43.17807341 2
 
< 0.1%
ValueCountFrequency (%)
114136.4102 18
 
0.2%
95698.89151 2
 
< 0.1%
73805.4992 19
 
0.2%
73134.37663 23
 
0.2%
70405.37084 21
 
0.2%
63799.26101 3
 
< 0.1%
62165.48183 5
 
< 0.1%
41978.10578 82
0.8%
30942.40928 105
1.0%
28962.60299 3
 
< 0.1%

country_costsum
Real number (ℝ)

HIGH CORRELATION 

Distinct217
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11730932
Minimum693.54338
Maximum1.4661471 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:43.533375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum693.54338
5-th percentile139020.26
Q11348093.3
median3915463.7
Q38886112.7
95-th percentile49600498
Maximum1.4661471 × 108
Range1.4661401 × 108
Interquartile range (IQR)7538019.4

Descriptive statistics

Standard deviation23483098
Coefficient of variation (CV)2.0018101
Kurtosis19.024598
Mean11730932
Median Absolute Deviation (MAD)3000868.5
Skewness4.0988546
Sum1.2111014 × 1011
Variance5.5145588 × 1014
MonotonicityNot monotonic
2024-03-14T18:23:43.594955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
802448.647 343
 
3.3%
5247787.762 298
 
2.9%
63439.20959 228
 
2.2%
28422812.46 210
 
2.0%
146614708.5 196
 
1.9%
25600163.35 185
 
1.8%
5847370.596 171
 
1.7%
9692430.206 167
 
1.6%
5882010.138 164
 
1.6%
67323748.88 149
 
1.4%
Other values (207) 8213
79.6%
ValueCountFrequency (%)
693.5433768 1
 
< 0.1%
1058.055385 1
 
< 0.1%
1058.103853 2
 
< 0.1%
1218.415706 7
0.1%
1227.934208 1
 
< 0.1%
1406.44964 2
 
< 0.1%
3896.307105 3
< 0.1%
4329.526827 6
0.1%
5023.03443 2
 
< 0.1%
5070.964957 2
 
< 0.1%
ValueCountFrequency (%)
146614708.5 196
1.9%
83953821.82 83
 
0.8%
67323748.88 149
1.4%
49600497.97 145
1.4%
44299928.34 67
 
0.6%
35589662.84 70
 
0.7%
33728085.7 140
1.4%
30135256.98 82
 
0.8%
28422812.46 210
2.0%
27575718.15 93
0.9%

country_costmean
Real number (ℝ)

HIGH CORRELATION 

Distinct217
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110061.08
Minimum174.05939
Maximum1011491.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-03-14T18:23:43.658264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum174.05939
5-th percentile2339.5004
Q124615.402
median45342.51
Q399041.863
95-th percentile451837.24
Maximum1011491.8
Range1011317.8
Interquartile range (IQR)74426.461

Descriptive statistics

Standard deviation173978.85
Coefficient of variation (CV)1.5807482
Kurtosis9.3265142
Mean110061.08
Median Absolute Deviation (MAD)27732.484
Skewness2.9517561
Sum1.1362706 × 109
Variance3.0268642 × 1010
MonotonicityNot monotonic
2024-03-14T18:23:43.720038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2339.500429 343
 
3.3%
17610.02605 298
 
2.9%
278.2421473 228
 
2.2%
135346.726 210
 
2.0%
748034.2271 196
 
1.9%
138379.2613 185
 
1.8%
34195.14968 171
 
1.7%
58038.50423 167
 
1.6%
35865.91547 164
 
1.6%
451837.2408 149
 
1.4%
Other values (207) 8213
79.6%
ValueCountFrequency (%)
174.0593865 7
 
0.1%
278.2421473 228
2.2%
529.0519263 2
 
< 0.1%
634.6577817 19
 
0.2%
693.5433768 1
 
< 0.1%
703.2248202 2
 
< 0.1%
721.5878045 6
 
0.1%
1058.055385 1
 
< 0.1%
1227.934208 1
 
< 0.1%
1298.769035 3
 
< 0.1%
ValueCountFrequency (%)
1011491.829 83
0.8%
995521.9968 18
 
0.2%
832038.8111 2
 
< 0.1%
748034.2271 196
1.9%
661192.9603 67
 
0.6%
656607.6431 21
 
0.2%
653576.2739 19
 
0.2%
639339.7429 23
 
0.2%
562903.4522 5
 
< 0.1%
554692.5408 3
 
< 0.1%

Interactions

2024-03-14T18:23:41.412263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:34.713858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.284842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.281054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.847915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.418834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.979223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.532792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.086148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.783086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.312998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.847233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.458481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:34.761671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.329865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.330446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.895774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.467228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.026928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.579374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.298968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.826383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.358849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.894705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.501035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:34.806042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.370691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.375932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.942699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.510959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.070562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.621961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.341483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.867861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.400494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.946177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.549151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:34.856651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.417903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.425847image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.993443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.560363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.119942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.671071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.390139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.918802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.449090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.996660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.597633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:34.909402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.465916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.475833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.043715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.610281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.169110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.720434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.437253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.965974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.495444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.046789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.644455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:34.961630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.511541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.525850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.093150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.656283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.217370image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.768608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.482760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.011519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.541177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.094955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.690172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.010945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.555897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.572302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.140695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.702368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.262737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.814843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.526921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.056025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.585802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.141264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.734828image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.056057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.599763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.620414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.187772image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.747590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.308692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.859880image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.571238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.100197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.630776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.187949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.775336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.099174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.640922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.665842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.231275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.790857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.351869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.902612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.611204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.141177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.672646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.230923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.824406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.142532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.683149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.709237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.275264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.833018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.395968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.950576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.651659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.181327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.713372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.274142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.868505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.186940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.726186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.754674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.321423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.878552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.439897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.994496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.693342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.224133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.755600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.319339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.914781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.239472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:35.772105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:36.802538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.372195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:37.933899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:38.488699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.041990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:39.739546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.270814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:40.803955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-14T18:23:41.366437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-14T18:23:43.764593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
country_costmeancountry_costsumcountry_insmeancountry_inssumcountry_qtycountcountry_qtymeancountry_qtysumcountry_valmeancountry_valsumcountry_weightmeancountry_weightsumdel_date_scheduled_yr
country_costmean1.0000.8680.8270.747-0.0170.8070.7640.8380.7640.8650.8370.097
country_costsum0.8681.0000.7620.8900.3910.7300.8720.7410.8940.7120.8890.036
country_insmean0.8270.7621.0000.8790.0090.9100.8480.9630.8610.8460.8360.027
country_inssum0.7470.8900.8791.0000.4110.8140.9480.8310.9760.7030.885-0.008
country_qtycount-0.0170.3910.0090.4111.000-0.0110.344-0.0450.391-0.1290.2400.031
country_qtymean0.8070.7300.9100.814-0.0111.0000.9050.9420.8510.8240.8020.167
country_qtysum0.7640.8720.8480.9480.3440.9051.0000.8500.9730.7250.8810.128
country_valmean0.8380.7410.9630.831-0.0450.9420.8501.0000.8620.8910.8500.127
country_valsum0.7640.8940.8610.9760.3910.8510.9730.8621.0000.7400.9120.081
country_weightmean0.8650.7120.8460.703-0.1290.8240.7250.8910.7401.0000.8950.235
country_weightsum0.8370.8890.8360.8850.2400.8020.8810.8500.9120.8951.0000.175
del_date_scheduled_yr0.0970.0360.027-0.0080.0310.1670.1280.1270.0810.2350.1751.000

Missing values

2024-03-14T18:23:41.978348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:23:42.061978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

country_qtycountdel_date_scheduled_yrcountry_qtysumcountry_qtymeancountry_valsumcountry_valmeancountry_inssumcountry_insmeancountry_weightsumcountry_weightmeancountry_costsumcountry_costmean
0142006295532110.928571370713.7026479.5500000.034300.00245055207.9140593943.4224339.418457e+0567274.693036
112200615299812749.8333331358232.36113186.0300000.029400.00245013594.4896921132.8741411.136473e+0694706.044620
2142006295532110.928571370713.7026479.5500000.034300.00245055207.9140593943.4224339.418457e+0567274.693036
312200615299812749.8333331358232.36113186.0300000.029400.00245013594.4896921132.8741411.136473e+0694706.044620
412200615299812749.8333331358232.36113186.0300000.029400.00245013594.4896921132.8741411.136473e+0694706.044620
5112006582075291.545455160146.5514558.7772730.026950.00245027914.0620862537.6420084.063337e+0536939.428492
6962007150696415697.54166716485986.84171729.02958317251.09575179.698914240889.7182222509.2678984.384424e+0645671.079118
7120061666716667.00000060834.5560834.5500000.002450.0024501590.2006831590.2006831.580671e+0415806.705846
8112006582075291.545455160146.5514558.7772730.026950.00245027914.0620862537.6420084.063337e+0536939.428492
9962007150696415697.54166716485986.84171729.02958317251.09575179.698914240889.7182222509.2678984.384424e+0645671.079118
country_qtycountdel_date_scheduled_yrcountry_qtysumcountry_qtymeancountry_valsumcountry_valmeancountry_inssumcountry_insmeancountry_weightsumcountry_weightmeancountry_costsumcountry_costmean
103141052015681133564869.85714364697700.08616168.57219067697.39644.7370483.248953e+0630942.4092842.634203e+07250876.519554
103151052015681133564869.85714364697700.08616168.57219067697.39644.7370483.248953e+0630942.4092842.634203e+07250876.519554
103161052015681133564869.85714364697700.08616168.57219067697.39644.7370483.248953e+0630942.4092842.634203e+07250876.519554
103171052015681133564869.85714364697700.08616168.57219067697.39644.7370483.248953e+0630942.4092842.634203e+07250876.519554
10318502015294314558862.90000022235886.28444717.72560023665.57473.3114004.029754e+058059.5080874.251579e+0685031.585415
10319502015294314558862.90000022235886.28444717.72560023665.57473.3114004.029754e+058059.5080874.251579e+0685031.585415
10320572015161077828259.26315813412251.62235302.66000014597.50256.0964917.008422e+0512295.4775556.228006e+06109263.259461
10321552015371310567511.00000029392921.40534416.75272730355.36551.9156361.262452e+0622953.6749171.412957e+07256901.347684
10322502015294314558862.90000022235886.28444717.72560023665.57473.3114004.029754e+058059.5080874.251579e+0685031.585415
10323502015294314558862.90000022235886.28444717.72560023665.57473.3114004.029754e+058059.5080874.251579e+0685031.585415

Duplicate rows

Most frequently occurring

country_qtycountdel_date_scheduled_yrcountry_qtysumcountry_qtymeancountry_valsumcountry_valmeancountry_inssumcountry_insmeancountry_weightsumcountry_weightmeancountry_costsumcountry_costmean# duplicates
205343201465366190.5714291283746.293742.7005541533.550004.4709911.108872e+0432.3286228.024486e+052339.500429343
20429820095950621996.8523497079591.2823757.01771813904.9000046.6607389.042055e+04303.4246535.247788e+0617610.026047298
20322820151519466.640351468161.632053.340482535.390002.3482022.068879e+039.0740316.343921e+04278.242147228
2022102010354812416895.82857133869606.52161283.84057172852.29000346.9156675.860529e+052790.7282432.842281e+07135346.726013210
20119620111543607478755.47959267742339.79345624.182602123010.43000627.6042352.735926e+0613958.8042651.466147e+08748034.227104196
2001852009244375113209.46486535452389.84191634.53967669622.10000376.3356761.079001e+065832.4393022.560016e+07138379.261330185
199171201015156318863.33918114919430.4187248.13105331461.79000183.9870763.945808e+052307.4899395.847371e+0634195.149682171
1981672013377957422632.17964128476197.72170516.15401235536.51000212.7934739.377679e+055615.3767189.692430e+0658038.504228167
197164200711452186983.03658512069370.4873593.72243914407.3700587.8498171.747381e+051065.4762055.882010e+0635865.915474164
1961492010582996339127.26845626199468.38175835.35825556469.83000378.9921489.296079e+056238.9793646.732375e+07451837.240822149